YOLO-NAS
Overview
Developed by Deci AI, YOLO-NAS is a groundbreaking object detection foundational model. It is the product of advanced Neural Architecture Search technology, meticulously designed to address the limitations of previous YOLO models. With significant improvements in quantization support and accuracy-latency trade-offs, YOLO-NAS represents a major leap in object detection.
Overview of YOLO-NAS. YOLO-NAS employs quantization-aware blocks and selective quantization for optimal performance. The model, when converted to its INT8 quantized version, experiences a minimal precision drop, a significant improvement over other models. These advancements culminate in a superior architecture with unprecedented object detection capabilities and outstanding performance.
Key Features
- Quantization-Friendly Basic Block: YOLO-NAS introduces a new basic block that is friendly to quantization, addressing one of the significant limitations of previous YOLO models.
- Sophisticated Training and Quantization: YOLO-NAS leverages advanced training schemes and post-training quantization to enhance performance.
- AutoNAC Optimization and Pre-training: YOLO-NAS utilizes AutoNAC optimization and is pre-trained on prominent datasets such as COCO, Objects365, and Roboflow 100. This pre-training makes it extremely suitable for downstream object detection tasks in production environments.
Pre-trained Models
Experience the power of next-generation object detection with the pre-trained YOLO-NAS models provided by Ultralytics. These models are designed to deliver top-notch performance in terms of both speed and accuracy. Choose from a variety of options tailored to your specific needs:
| Model | mAP | Latency (ms) |
|---|---|---|
| YOLO-NAS S | 47.5 | 3.21 |
| YOLO-NAS M | 51.55 | 5.85 |
| YOLO-NAS L | 52.22 | 7.87 |
| YOLO-NAS S INT-8 | 47.03 | 2.36 |
| YOLO-NAS M INT-8 | 51.0 | 3.78 |
| YOLO-NAS L INT-8 | 52.1 | 4.78 |
Each model variant is designed to offer a balance between Mean Average Precision (mAP) and latency, helping you optimize your object detection tasks for both performance and speed.
Usage Examples
Ultralytics has made YOLO-NAS models easy to integrate into your Python applications via our ultralytics python package. The package provides a user-friendly Python API to streamline the process.
The following examples show how to use YOLO-NAS models with the ultralytics package for inference and validation:
Inference and Validation Examples
In this example we validate YOLO-NAS-s on the COCO8 dataset.
Example
This example provides simple inference and validation code for YOLO-NAS. For handling inference results see Predict mode. For using YOLO-NAS with additional modes see Val and Export. YOLO-NAS on the ultralytics package does not support training.
PyTorch pretrained *.pt models files can be passed to the NAS() class to create a model instance in python:
from ultralytics import NAS
# Load a COCO-pretrained YOLO-NAS-s model
model = NAS("yolo_nas_s.pt")
# Display model information (optional)
model.info()
# Validate the model on the COCO8 example dataset
results = model.val(data="coco8.yaml")
# Run inference with the YOLO-NAS-s model on the 'bus.jpg' image
results = model("path/to/bus.jpg")
CLI commands are available to directly run the models:
Supported Tasks and Modes
We offer three variants of the YOLO-NAS models: Small (s), Medium (m), and Large (l). Each variant is designed to cater to different computational and performance needs:
- YOLO-NAS-s: Optimized for environments where computational resources are limited but efficiency is key.
- YOLO-NAS-m: Offers a balanced approach, suitable for general-purpose object detection with higher accuracy.
- YOLO-NAS-l: Tailored for scenarios requiring the highest accuracy, where computational resources are less of a constraint.
Below is a detailed overview of each model, including links to their pre-trained weights, the tasks they support, and their compatibility with different operating modes.
| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
|---|---|---|---|---|---|---|
| YOLO-NAS-s | yolo_nas_s.pt | Object Detection | ✅ | ✅ | ❌ | ✅ |
| YOLO-NAS-m | yolo_nas_m.pt | Object Detection | ✅ | ✅ | ❌ | ✅ |
| YOLO-NAS-l | yolo_nas_l.pt | Object Detection | ✅ | ✅ | ❌ | ✅ |
Citations and Acknowledgements
If you employ YOLO-NAS in your research or development work, please cite SuperGradients:
@misc{supergradients,
doi = {10.5281/ZENODO.7789328},
url = {https://zenodo.org/record/7789328},
author = {Aharon, Shay and {Louis-Dupont} and {Ofri Masad} and Yurkova, Kate and {Lotem Fridman} and {Lkdci} and Khvedchenya, Eugene and Rubin, Ran and Bagrov, Natan and Tymchenko, Borys and Keren, Tomer and Zhilko, Alexander and {Eran-Deci}},
title = {Super-Gradients},
publisher = {GitHub},
journal = {GitHub repository},
year = {2021},
}
We express our gratitude to Deci AI's SuperGradients team for their efforts in creating and maintaining this valuable resource for the computer vision community. We believe YOLO-NAS, with its innovative architecture and superior object detection capabilities, will become a critical tool for developers and researchers alike.
FAQ
What is YOLO-NAS and how does it improve over previous YOLO models?
YOLO-NAS, developed by Deci AI, is a state-of-the-art object detection model leveraging advanced Neural Architecture Search (NAS) technology. It addresses the limitations of previous YOLO models by introducing features like quantization-friendly basic blocks and sophisticated training schemes. This results in significant improvements in performance, particularly in environments with limited computational resources. YOLO-NAS also supports quantization, maintaining high accuracy even when converted to its INT8 version, enhancing its suitability for production environments. For more details, see the Overview section.
How can I integrate YOLO-NAS models into my Python application?
You can easily integrate YOLO-NAS models into your Python application using the ultralytics package. Here's a simple example of how to load a pre-trained YOLO-NAS model and perform inference:
from ultralytics import NAS
# Load a COCO-pretrained YOLO-NAS-s model
model = NAS("yolo_nas_s.pt")
# Validate the model on the COCO8 example dataset
results = model.val(data="coco8.yaml")
# Run inference with the YOLO-NAS-s model on the 'bus.jpg' image
results = model("path/to/bus.jpg")
For more information, refer to the Inference and Validation Examples.
What are the key features of YOLO-NAS and why should I consider using it?
YOLO-NAS introduces several key features that make it a superior choice for object detection tasks:
- Quantization-Friendly Basic Block: Enhanced architecture that improves model performance with minimal precision drop post quantization.
- Sophisticated Training and Quantization: Employs advanced training schemes and post-training quantization techniques.
- AutoNAC Optimization and Pre-training: Utilizes AutoNAC optimization and is pre-trained on prominent datasets like COCO, Objects365, and Roboflow 100. These features contribute to its high accuracy, efficient performance, and suitability for deployment in production environments. Learn more in the Key Features section.
Which tasks and modes are supported by YOLO-NAS models?
YOLO-NAS models support various object detection tasks and modes such as inference, validation, and export. They do not support training. The supported models include YOLO-NAS-s, YOLO-NAS-m, and YOLO-NAS-l, each tailored to different computational capacities and performance needs. For a detailed overview, refer to the Supported Tasks and Modes section.
Are there pre-trained YOLO-NAS models available and how do I access them?
Yes, Ultralytics provides pre-trained YOLO-NAS models that you can access directly. These models are pre-trained on datasets like COCO, ensuring high performance in terms of both speed and accuracy. You can download these models using the links provided in the Pre-trained Models section. Here are some examples:
